GeologyParcelAndFloodGIS/Building_Footprint_2022

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Building Footprint 2022 (240)

Description: This dataset provides a statewide collection of building footprint polygons for Mississippi. The core geometry was derived from BING Raster imagery by Microsoft. MARIS obtained the original dataset in JSON format and converted it to a WGS84 Geographic Coordinate System shapefile using MapShaper software. The dataset was subsequently reprojected into the NAD 1983 Mississippi Transverse Mercator (MSTM) coordinate system (Meters). The 2022 release contains approximately 1,507,496 building footprint polygons statewide.Key Components:Geometry: The dataset contains over 1.5 million structure polygons representing various building footprints. After quality control, the dataset was reduced to 1,502,581 structures by removing approximately 4,900 footprints located outside the state boundary and 15 polygons that were inaccurately positioned on roadways.Elevation Integration: Each polygon has been attributed with Z-values derived from statewide LIDAR datasets. This includes calculated values such as Ground Elevation (at the base of the structure).Parcel Linkage: Footprints are spatially joined to the Statewide Cadastral Framework, providing a link between physical structures and county tax assessor records (Parcels).Coordinate System: Projected in Mississippi State TM (MSTM).Data Lineage & Processing: The original Microsoft footprints were extracted using automated computer vision techniques and subsequently underwent a series of workflows. Users should be aware that while the data is highly comprehensive, accuracy is dependent on the date of the source imagery and LIDAR Dataset. MS Building Footprint polygons - 2022 ***** See source download and supplemental information for details on data creation by Microsoft. https://github.com/Microsoft/USBuildingFootprintsMicrosoft's explanation: “The gap areas contain image tiles taken with different cameras, which is causing the creation of artificial edges between neighboring tiles. These confuse our detection network, which hasn't learned to deal with them. We took a very conservative approach of skipping such tiles. I think we could add additional effort to properly deal with this problem.” LIDAR Data Source Year and Project Name:T2013_Lauderdale_LidarT2014_Central_Mississippi_LidarT2015_Coastal_Lidar___ContoursT2015_Southeast_Mississippi_Lidar_UTM15T2015_Southeast_Mississippi_Lidar_UTM16T2015_South_Central_Lidar_UTM15T2015_South_Central_Lidar_UTM16T2016_Southwest_LidarT2016_Camp_Shelby___ContoursT2016_Tishomingo_Lidar___ContoursT2018_Madison_Rankin_Lidar___ContoursT2018_Rankin_Simpson_Lidar___ContoursT2018_Tenn_Tom_Lidar___ContoursT2022_Delta_Lidar_UTM15T2022_Delta_Lidar_UTM16

Service Item Id: 378d6b3ee9144f0d9a4a7bc15c2b0783

Copyright Text: Microsoft, BING, MARIS

Spatial Reference: 0 (0) LatestVCSWkid(0)

Single Fused Map Cache: false

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Units: esriMeters

Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP

Document Info:

Supports Dynamic Layers: true

MaxRecordCount: 2000

MaxImageHeight: 4096

MaxImageWidth: 4096

Supported Query Formats: JSON, geoJSON, PBF

Supports Query Data Elements: true

Min Scale: 0

Max Scale: 0

Supports Datum Transformation: true

Child Resources: Info Dynamic Layer

Supported Operations: Export Map Identify QueryLegends QueryDomains Find Return Updates

GeologyParcelAndFloodGIS/Building_Footprint_2022_Elev (MapServer)